The subject matter described herein relates to systems and methods for processing images obtained during an optical colonoscopy procedure, and, more particularly, to an image quality assessment system and method to automatically evaluate the processed images and assign an informativeness score to each image.
Colorectal cancer (CRC) is the second highest cause of cancer-related deaths in the United States with 50,830 estimated deaths in 2013. A majority of these deaths may have been prevented by using a high quality screening test. More than 80% of CRC cases arise from adenomatous polyps, which are precancerous abnormal growths of the colon wall. The preferred screening method for polyp detection and removal is an optical colonoscopy (OC) procedure, during which a colonoscopist meticulously examines the colon wall using a tiny camera that is inserted and guided through the colon. The goal of an OC is to detect and remove colorectal polyps, which may be precursors to CRC. It has been shown that timely removal of polyps can significantly reduce the mortality of CRC.
However, polyp detection with an OC remains a challenging task and, as evidenced by several clinical studies, a significant portion of flat, sessile, and pedunculated polyps remain undetected during colon screening with an OC. OC as the primary modality for screening and preventing colorectal cancer is still far from ideal with polyps and cancers being missed during procedures. A Canadian study reports a 6% cancer miss-rate during colonoscopy and attributes this to the polyps that are missed due to insufficient quality of procedures. This is due to the fact that the effectiveness of a colonoscopy for polyp detection highly depends on the visibility of the images that are captured by the camera and subsequently displayed on a monitor. Therefore, higher quality images taken during a colonoscopy allow for a higher quality colonoscopy procedure. Existing methods for making objective quality assessments use a gray level co-occurrence matrix (GLCM) in the Fourier domain and/or two-dimensional (2D) discrete wavelet transform (DWT) in the spatial domain. However, these methods both fail to achieve high accuracy for a collected dataset.
Further, an OC is also an operator-dependent task wherein the quality of the examination depends on the colonoscopist's level of diligence and attentiveness during the colon examination. The quality of a colonoscopy procedure is currently assessed by measuring the total examination time. However, the total examination time is not informative enough to completely reflect the quality of a procedure. For example, a colonoscopist may spend a large amount of time in one segment of the colon but perform a quick examination in other parts of the colon. Therefore, in analyzing the metrics of the particular procedure described above, an evaluator may note the long examination time and correlate this long examination time with a thorough procedure.
Therefore there is a need for a system and associated method to overcome the inherent difficulties in assessing the quality of an examination by analyzing a length of time required to complete an examination. There is also a need for an image quality assessment for colonoscopy which provides feedback to an operator of the quality of the images taken during a colonoscopy procedure and then, subsequently, the overall quality of the procedure based on the information captured in the images.